Integration of genetic algorithms and GIS for optimal location search

Abstract

Optimal location search is frequently required in many urban applications for siting one or more facilities. However, the search may become very complex when it involves multiple sites, various constraints and multiple‐objectives. The exhaustive blind (brute‐force) search with high‐dimensional spatial data is infeasible in solving optimization problems because of a huge combinatorial solution space. Intelligent search algorithms can help to improve the performance of spatial search. This study will demonstrate that genetic algorithms can be used with Geographical Information systems (GIS) to effectively solve the spatial decision problems for optimally sitting n sites of a facility. Detailed population and transportation data from GIS are used to facilitate the calculation of fitness functions. Multiple planning objectives are also incorporated in the GA program. Experiments indicate that the proposed method has much better performance than simulated annealing and GIS neighborhood search methods. The GA method is very convenient in finding the solution with the highest utility value.

Keywords:

• Genetic algorithms
• GIS
• optimal location
• multiple objectives
• simulated annealing

Acknowledgement

This study is supported by the National Natural Science Foundation of China (NSFC) (projected No. 40471105), the ‘985’ Project of GIS and Remote Sensing for Geosciences from the Ministry of Education of China, and the PhD development program from the Ministry of Education of China (Project No. 20040558023).